






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase__      			=						16
UpperCAmelCase__      			=						32







def     a_   (__A				,       __A = 16				,       __A = "bert-base-cased"      )				->						List[Any]:



					"""simple docstring"""
					__a					:						str      =					AutoTokenizer.from_pretrained(lowerCamelCase_      )
					__a					:						str      =					load_dataset("glue"				,       "mrpc"      )

					def tokenize_function(__A      ):
										# max_length=None => use the model max length (it's actually the default)
										__a					:						Union[str, Any]      =					tokenizer(examples["sentence1"]				,       examples["sentence2"]				,       truncation=lowerCamelCase_				,       max_length=lowerCamelCase_      )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					__a					:						Union[str, Any]      =					datasets.map(
					    lowerCamelCase_				,       batched=lowerCamelCase_				,       remove_columns=["idx", "sentence1", "sentence2"]				,       load_from_cache_file=lowerCamelCase_      )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__a					:						List[Any]      =					tokenized_datasets.rename_column("label"				,       "labels"      )

					def collate_fn(__A      ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(lowerCamelCase_				,       padding="max_length"				,       max_length=128				,       return_tensors="pt"      )
										return tokenizer.pad(lowerCamelCase_				,       padding="longest"				,       return_tensors="pt"      )

					# Instantiate dataloaders.
					__a					:						Dict      =					DataLoader(
					    tokenized_datasets["train"]				,       shuffle=lowerCamelCase_				,       collate_fn=lowerCamelCase_				,       batch_size=lowerCamelCase_      )
					__a					:						Union[str, Any]      =					DataLoader(
					    tokenized_datasets["validation"]				,       shuffle=lowerCamelCase_				,       collate_fn=lowerCamelCase_				,       batch_size=lowerCamelCase_      )

					return train_dataloader, eval_dataloader







def     a_   (__A				,       __A      )				->						Any:



					"""simple docstring"""
					# Initialize accelerator
					__a					:						Tuple      =					Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__a					:						Optional[int]      =					config['''lr''']
					__a					:						Optional[Any]      =					int(config["num_epochs"]      )
					__a					:						Optional[Any]      =					int(config["seed"]      )
					__a					:						Any      =					int(config["batch_size"]      )
					__a					:						List[str]      =					args.model_name_or_path

					set_seed(lowerCamelCase_      )
					__a					:						List[Any]      =					get_dataloaders(lowerCamelCase_				,       lowerCamelCase_				,       lowerCamelCase_      )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__a					:						Optional[Any]      =					AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_				,       return_dict=lowerCamelCase_      )

					# Instantiate optimizer
					__a					:						Dict      =					(
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					__a					:						Optional[int]      =					optimizer_cls(params=model.parameters()				,       lr=lowerCamelCase_      )

					if accelerator.state.deepspeed_plugin is not None:
										__a					:						Optional[int]      =					accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										__a					:						Tuple      =					1
					__a					:						int      =					(len(lowerCamelCase_      ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										__a					:						Dict      =					get_linear_schedule_with_warmup(
										    optimizer=lowerCamelCase_				,       num_warmup_steps=0				,       num_training_steps=lowerCamelCase_				,       )
					else:
										__a					:						Optional[int]      =					DummyScheduler(lowerCamelCase_				,       total_num_steps=lowerCamelCase_				,       warmup_num_steps=0      )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					__a					:						Union[str, Any]      =					accelerator.prepare(
					    lowerCamelCase_				,       lowerCamelCase_				,       lowerCamelCase_				,       lowerCamelCase_				,       lowerCamelCase_      )

					# We need to keep track of how many total steps we have iterated over
					__a					:						Optional[Any]      =					0
					# We also need to keep track of the stating epoch so files are named properly
					__a					:						Union[str, Any]      =					0

					# Now we train the model
					__a					:						Optional[int]      =					evaluate.load("glue"				,       "mrpc"      )
					__a					:						Tuple      =					0
					__a					:						List[str]      =					{}
					for epoch in range(lowerCamelCase_				,       lowerCamelCase_      ):
										model.train()
										for step, batch in enumerate(lowerCamelCase_      ):
															__a					:						Optional[int]      =					model(**lowerCamelCase_      )
															__a					:						Optional[Any]      =					outputs.loss
															__a					:						List[Any]      =					loss / gradient_accumulation_steps
															accelerator.backward(lowerCamelCase_      )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

										model.eval()
										__a					:						Optional[Any]      =					0
										for step, batch in enumerate(lowerCamelCase_      ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device      )
															with torch.no_grad():
																				__a					:						Optional[Any]      =					model(**lowerCamelCase_      )
															__a					:						List[str]      =					outputs.logits.argmax(dim=-1      )
															# It is slightly faster to call this once, than multiple times
															__a					:						Optional[int]      =					accelerator.gather(
															    (predictions, batch["labels"])      )  # If we are in a multiprocess environment, the last batch has duplicates
															if accelerator.use_distributed:
																				if step == len(lowerCamelCase_      ) - 1:
																									__a					:						List[str]      =					predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																									__a					:						str      =					references[: len(eval_dataloader.dataset      ) - samples_seen]
																				else:
																									samples_seen += references.shape[0]
															metric.add_batch(
															    predictions=lowerCamelCase_				,       references=lowerCamelCase_				,       )

										__a					:						List[Any]      =					metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'epoch {epoch}:'				,       lowerCamelCase_      )
										__a					:						Any      =					eval_metric['''accuracy''']

										if best_performance < eval_metric["accuracy"]:
															__a					:						str      =					eval_metric['''accuracy''']

					if args.performance_lower_bound is not None:
										assert (
										    args.performance_lower_bound <= best_performance
										), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

					accelerator.wait_for_everyone()
					if accelerator.is_main_process:
										with open(os.path.join(args.output_dir				,       "all_results.json"      )				,       "w"      ) as f:
															json.dump(lowerCamelCase_				,       lowerCamelCase_      )







def     a_   ()				->						Optional[Any]:



					"""simple docstring"""
					__a					:						Optional[Any]      =					argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
					parser.add_argument(
					    "--model_name_or_path"				,       type=lowerCamelCase_				,       default="bert-base-cased"				,       help="Path to pretrained model or model identifier from huggingface.co/models."				,       required=lowerCamelCase_				,       )
					parser.add_argument(
					    "--output_dir"				,       type=lowerCamelCase_				,       default="."				,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."				,       )
					parser.add_argument(
					    "--performance_lower_bound"				,       type=lowerCamelCase_				,       default=lowerCamelCase_				,       help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."				,       )
					parser.add_argument(
					    "--num_epochs"				,       type=lowerCamelCase_				,       default=3				,       help="Number of train epochs."				,       )
					__a					:						Optional[int]      =					parser.parse_args()
					__a					:						str      =					{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
					training_function(lowerCamelCase_				,       lowerCamelCase_      )


if __name__ == "__main__":
			main()








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase			:				Optional[int]   =     logging.get_logger(__name__)

lowercase			:				Optional[Any]   =     {"vocab_file": "vocab.txt"}

lowercase			:				Optional[int]   =     {
    "vocab_file": {
        "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
        "YituTech/conv-bert-medium-small": (
            "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
        ),
        "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
    }
}

lowercase			:				Tuple   =     {
    "YituTech/conv-bert-base": 5_12,
    "YituTech/conv-bert-medium-small": 5_12,
    "YituTech/conv-bert-small": 5_12,
}


lowercase			:				Optional[int]   =     {
    "YituTech/conv-bert-base": {"do_lower_case": True},
    "YituTech/conv-bert-medium-small": {"do_lower_case": True},
    "YituTech/conv-bert-small": {"do_lower_case": True},
}







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	VOCAB_FILES_NAMES
							UpperCAmelCase_						:		Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_						:		List[Any]								=	PRETRAINED_INIT_CONFIGURATION
							UpperCAmelCase_						:		Optional[Any]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_						:		Optional[int]								=	ConvBertTokenizer




							def __init__(					self    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase="[UNK]"    ,						__UpperCAmelCase="[SEP]"    ,						__UpperCAmelCase="[PAD]"    ,						__UpperCAmelCase="[CLS]"    ,						__UpperCAmelCase="[MASK]"    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase    ,						)		->       Optional[int]:
														super().__init__(
														    __UpperCAmelCase    ,						tokenizer_file=__UpperCAmelCase    ,						do_lower_case=__UpperCAmelCase    ,						unk_token=__UpperCAmelCase    ,						sep_token=__UpperCAmelCase    ,						pad_token=__UpperCAmelCase    ,						cls_token=__UpperCAmelCase    ,						mask_token=__UpperCAmelCase    ,						tokenize_chinese_chars=__UpperCAmelCase    ,						strip_accents=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)

														A      :					Any      		=					json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
														if (
														    normalizer_state.get('''lowercase'''    ,						__UpperCAmelCase						) != do_lower_case
														    or normalizer_state.get('''strip_accents'''    ,						__UpperCAmelCase						) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''    ,						__UpperCAmelCase						) != tokenize_chinese_chars
														):
																					A      :					Tuple      		=					getattr(__UpperCAmelCase    ,						normalizer_state.pop('''type'''						)						)
																					A      :					int      		=					do_lower_case
																					A      :					int      		=					strip_accents
																					A      :					Union[str, Any]      		=					tokenize_chinese_chars
																					A      :					str      		=					normalizer_class(**__UpperCAmelCase						)

														A      :					str      		=					do_lower_case




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None						)		->       Optional[int]:
														A      :					Dict      		=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       List[int]:
														A      :					List[Any]      		=					[self.sep_token_id]
														A      :					Optional[Any]      		=					[self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep						) * [0]
														return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       Tuple[str]:
														A      :					Optional[int]      		=					self._tokenizer.model.save(__UpperCAmelCase    ,						name=__UpperCAmelCase						)
														return tuple(__UpperCAmelCase						)






